For lots of services in information, data, telecommunications and/or traditional non-information fields, the accurate estimation and analysis of service demands are of vital importance. In particular, many services are spatially distributed, such as mobile communication services, network services, etc. For such services, how to accurately analyze and predict service demands at different locations and/or different moments plays a significant role in the allocation and planning of service resources, for example.
Consider mobile communication services as an example. In a wireless communication network formed by cells, the service capacity (e.g., coverage scope, the number of mobile terminals that can be processed, etc.) of each local access point (AP) is typically limited. It depends on the service demand prediction to a great extent regarding how and what access points are disposed at various locations of the wireless network and how these access points are activated or deactivated at different periods of time. Specifically, if it is possible to determine by analysis that a service demanded at a certain location is obviously higher than those at other locations, then it is possible to accordingly adjust the number of deployed APs, the type thereof, and/or the service capacity, etc.
Known service demand analysis includes statistics of service situation at various locations in a space where services are distributed. Based on the statistical information, it is possible to analyze the service demand situation at each location. However, according to the prior art, an event triggering a service is always assumed to be predictable in the demand analysis of spatially distributed services. Hence, different characteristics and different aspects of such events are not fully considered in the service analysis and subsequent optimization. Besides, spatial heterogeneity in the worst case scenarios is difficult to estimate according to known demand analysis schemes. Sharing of flexible resources (e.g., temporal resources) is also difficult. Moreover, known demand event analysis schemes fail to integrate the fact that demands vary with the location and time. Therefore, the location-based demand statistics and analysis will not be efficiently updated or changed with time.